Search results for: entropy measure

Authors: Aly Elshafei, Daniela Romano

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With the development of autonomous vehicles, a human-machine interaction (HMI) system is needed for a safe transition of control when a takeover request (TOR) is required. An important part of the HMI system is the ability to monitor the level of situational awareness (SA) of any driver in real-time, in different scenarios, and without any pre-calibration. Presenting state-of-the-art machine learning models used to measure SA is the purpose of this systematic review. Investigating the limitations of each type of sensor, the gaps, and the most suited sensor and computational model that can be used in driving applications. To the author’s best knowledge this is the first literature review identifying online and offline classification methods used to measure SA, explaining which measurements are subject or session-specific, and how many classifications can be done with each classification model. This information can be very useful for researchers measuring SA to identify the most suited model to measure SA for different applications.

Keywords: situational awareness, autonomous driving, gaze metrics, EEG, ECG

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Authors: Karan Sharma, Ajay Kumar

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Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether.

Keywords: EEG signal, Reiki, time consuming, epileptic seizure

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Authors: Mazhar B. Tayel, Eslam I. AlSaba

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The heart is the most important part in any body organisms. It effects and affected by any factor in the body. Therefore, it is a good detector of any matter in the body. When the heart signal is non-stationary signal, therefore, it should be study its variability. So, the Heart Rate Variability (HRV) has attracted considerable attention in psychology, medicine and have become important dependent measure in psychophysiology and behavioral medicine. Quantification and interpretation of heart rate variability. However, remain complex issues are fraught with pitfalls. This paper presents one of the non-linear techniques to analyze HRV. It discusses 'What Poincare plot is?', 'How it is work?', 'its usage benefits especially in HRV', 'the limitation of Poincare cause of standard deviation SD1, SD2', and 'How overcome this limitation by using complex correlation measure (CCM)'. The CCM is most sensitive to changes in temporal structure of the Poincaré plot as compared to SD1 and SD2.

Keywords: heart rate variability, chaotic system, poincare, variance, standard deviation, complex correlation measure

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Authors: T. Gomes, J. Manzi

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The inclusion of stirring systems in the calculation and optimization procedures has been undergone a significant lack of attention, what it can reflect in the results because such systems provide an additional energy to the process, besides promote a better distribution of mass and energy. This is meaningful for the reactive systems, particularly for the Continuous Stirred Tank Reactor (CSTR), for which the key variables and parameters, as well as the operating conditions of stirring systems, can play a pivotal role and it has been showed in the literature that neglect these factors can lead to sub-optimal results. It is also well known that the sole use of the First Law of Thermodynamics as an optimization tool cannot yield satisfactory results, since the joint use of the First and Second Laws condensed into a procedure so-called entropy generation minimization (EGM) has shown itself able to drive the system towards better results. Therefore, the main objective of this paper is to determine the effects of key parameters of the stirring system in the optimization procedures by means of EGM applied to the reactive systems. Such considerations have been possible by dimensional analysis according to Rayleigh and Buckingham's method, which takes into account the physical and geometric parameters and the variables of the reactive system. For the simulation purpose based on the production of propylene glycol, the results have shown a significant increase in the conversion rate from 36% (not-optimized system) to 95% (optimized system) with a consequent reduction of by-products. In addition, it has been possible to establish the influence of the work of the stirrer in the optimization procedure, in which can be described as a function of the fluid viscosity and consequently of the temperature. The conclusions to be drawn also indicate that the use of the entropic analysis as optimization tool has been proved to be simple, easy to apply and requiring low computational effort.

Keywords: stirring systems, entropy, reactive system, optimization

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Authors: Ahmed Masrai

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Listening comprehension constitutes a considerable challenge for the second language (L2) learners, but a little is known about the explanatory power of different variables in explaining variance in listening comprehension. Since research in this area, to the researcher's knowledge, is relatively small in comparison to that focusing on the relationship between reading comprehension and factors such as vocabulary and working memory, there is a need for studies that are seeking to fill the gap in our knowledge about the specific contribution of working memory capacity (WMC), aural vocabulary knowledge and written vocabulary knowledge to explaining listening comprehension. Among 130 English as foreign language learners, the present study examines what proportion of the variance in listening comprehension is explained by aural vocabulary knowledge, written vocabulary knowledge, and WMC. Four measures were used to collect the required data for the study: (1) A-Lex, a measure of aural vocabulary knowledge; (2) XK-Lex, a measure of written vocabulary knowledge; (3) Listening Span Task, a measure of WMC and; (4) IELTS Listening Test, a measure of listening comprehension. The results show that aural vocabulary knowledge is the strongest predictor of listening comprehension, followed by WMC, while written vocabulary knowledge is the weakest predictor. The study discusses implications for the explanatory power of aural vocabulary knowledge and WMC to listening comprehension and pedagogical practice in L2 classrooms.

Keywords: listening comprehension, second language, vocabulary knowledge, working memory

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Authors: Forrest Kaatz, Adhemar Bultheel

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We use an informational statistical mechanics approach to study the catalytic thermodynamics of platinum and palladium cuboctahedral nanoclusters. Nanoclusters and their adatoms are viewed as chemical graphs with a nearest neighbor adjacency matrix. We use the Morse potential to determine bond energies between cluster atoms in a coordination type calculation. We use adsorbate energies calculated from density functional theory (DFT) to study the adatom effects on the thermodynamic quantities, which are derived from a Hamiltonian. Oxygen radical and molecular adsorbates are studied on platinum clusters and hydrogen on palladium clusters. We calculate the entropy, free energy, and total energy as the coverage of adsorbates increases from bridge and hollow sites on the surface. Thermodynamic behavior versus adatom coverage is related to the structural distribution of adatoms on the nanocluster surfaces. The thermodynamic functions are characterized using a simple adsorption model, with linear trends as the coverage of adatoms increases. The data exhibits size effects for the measured thermodynamic properties with cluster diameters between 2 and 5 nm. Entropy and enthalpy calculations of Pt-O2 compare well with previous theoretical data for Pt(111)-O2, and our Pd-H results show similar trends as experimental measurements for Pd-H2 nanoclusters. Our methods are general and may be applied to wide variety of nanocluster adsorbate systems.

Keywords: catalytic thermodynamics, palladium nanocluster absorbates, platinum nanocluster absorbates, statistical mechanics

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Authors: Todd Zhou, Mikhail Yurochkin

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Out-of-distribution (OOD) detection is receiving increasing amounts of attention in the machine learning research community, boosted by recent technologies, such as autonomous driving and image processing. This newly-burgeoning field has called for the need for more effective and efficient methods for out-of-distribution generalization methods. Without accessing the label information, deploying machine learning models to out-of-distribution domains becomes extremely challenging since it is impossible to evaluate model performance on unseen domains. To tackle this out-of-distribution detection difficulty, we designed a model selection pipeline algorithm and developed a model selection framework with different impurity and randomness measurements to evaluate and choose the best-performing models for out-of-distribution data. By exploring different randomness scores based on predicted probabilities, we adopted the out-of-distribution entropy and developed a custom-designed score, ”CombinedScore,” as the evaluation criterion. This proposed score was created by adding labeled source information into the judging space of the uncertainty entropy score using harmonic mean. Furthermore, the prediction bias was explored through the equality of opportunity violation measurement. We also improved machine learning model performance through model calibration. The effectiveness of the framework with the proposed evaluation criteria was validated on the Folktables American Community Survey (ACS) datasets.

Keywords: model selection, domain generalization, model fairness, randomness measurements, bias index

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Authors: Vano Tsertsvadze, Marina Chavchanidze, Lali Khurtsia

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Problem of drug use is often seen as a combination of psychological and social problems, but this problem can be considered as economically rational decision in the process of buying pleasure (looking after children, reading, harvesting fruits in the fall, sex, eating, etc.). Before the adoption of the decisions people face to a trade-off - when someone chooses a delicious meal, she takes a completely rational decision, that the pleasure of eating has a lot more value than the pleasure which she will experience after two months diet on the summer beach showing off her beautiful body. This argument is also true for alcohol, drugs and cigarettes. Smoking has a negative effect on health, but smokers are not afraid of the threat of a lung cancer after 40 years, more valuable moment is a pleasure from smoking. Our hypothesis - unsatisfied pleasure and frustration, probably determines the risk of dependence on drug abuse. The purpose of research: 1- to determine the relative measure unit of pleasure, which will be used to measure and assess the intensity of various human pleasures. 2- to compare the intensity of the pleasure from different kinds of activity, with pleasures received from drug use. 3- Based on the analysis of data, to identify factors affecting the rational decision making. Research method: Respondents will be asked to recall the greatest pleasure of their life, which will be used as a measure of the other pleasures. The study will use focus groups and structured interviews.

Keywords: drug, drug-user, measurement, satisfaction

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Authors: Michele Aleandri, Matteo Colangeli, Davide Gabrielli

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We study a generalization to a continuous setting of the classical Markov chain tree theorem. In particular, we consider an irreducible diffusion process on a metric graph. The unique invariant measure has an atomic component on the vertices and an absolutely continuous part on the edges. We show that the corresponding density at x can be represented by a normalized superposition of the weights associated to metric arborescences oriented toward the point x. A metric arborescence is a metric tree oriented towards its root. The weight of each oriented metric arborescence is obtained by the product of the exponential of integrals of the form ∫a/b², where b is the drift and σ² is the diffusion coefficient, along the oriented edges, for a weight for each node determined by the local orientation of the arborescence around the node and for the inverse of the diffusion coefficient at x. The metric arborescences are obtained by cutting the original metric graph along some edges.

Keywords: diffusion processes, metric graphs, invariant measure, reversibility

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Authors: Shafaq Rana, Hina Ansar

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Projectification is an emerging phenomenon in Western economies. The projects have become the key driver of the economic actions. The impact of projectification is understudy for over a decade. A methodology was developed to measure the degree of projectification at economical level, which was later adapted to measure the degree of projectification in Germany, Norway, and Iceland; and compared the differences in these project societies, considering their industrial structure, organizational size, and the share of project work. Using the same methodology, this study aims to provide empirical evidence of the project work in the context of Pakistan –a developing nation, keeping into consideration the macroeconomic measures, qualitative and quantitative measures of the project i/c GDP, monetary measures, and project success. The research includes a qualitative pre-study to define these macro-measures in the country-specific context and a quantitative study to measure the project work w.r.t hours working in the organizations on projects. The outcome of this study provides the key data on the projectification in a developing economy, which will help industry practitioners and decision-makers to examine the consequences of projectification and strategize, respectively. This study also provides a foundation for further research in individual sectors of the country while exploring different macroeconomic questions, including the effect of projectification on project productivity, income effects, and labor market.

Keywords: developing economy, Pakistan, project work, projectification

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Authors: Supachai Supmak, Yachai Limpiyakorn

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Mutation testing can be applied for the quality assessment of test cases. Prioritization of mutation test generation has been a critical element of the industry practice that would contribute to the evaluation of test cases. The industry generally delivers the product under the condition of time to the market and thus, inevitably sacrifices software testing tasks, even though many test cases are required for software verification. This paper presents an approach of applying a social network centrality measure, PageRank, to prioritize mutation test generation. The source code with the highest values of PageRank will be focused first when developing their test cases as these modules are vulnerable to defects or anomalies which may cause the consequent defects in many other associated modules. Moreover, the approach would help identify the reducible test cases in the test suite, still maintaining the same criteria as the original number of test cases.

Keywords: software testing, mutation test, network centrality measure, test case prioritization

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Authors: Yagya Murti Aryal, Bipin Adhikari, Pradeep Gyawali

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The Lesser Himalaya zone of Nepal consists of thrusting and folding belts, which play an important role in the sustainable management of groundwater in the Himalayan regions. The study area is located in the Dolakha and Ramechhap Districts of Bagmati Province, Nepal. Geologically, these districts are situated in the Lesser Himalayas and partly encompass the Higher Himalayan rock sequence, which includes low-grade to high-grade metamorphic rocks. Following the Gorkha Earthquake in 2015, numerous springs dried up, and many others are currently experiencing depletion due to the distortion of the natural groundwater flow. The primary objective of this study is to identify potential groundwater areas and determine suitable sites for artificial groundwater recharge. Two distinct statistical approaches were used to develop models: The Frequency Ratio (FR) and Shannon Entropy (SE) methods. The study utilized both primary and secondary datasets and incorporated significant role and controlling factors derived from field works and literature reviews. Field data collection involved spring inventory, soil analysis, lithology assessment, and hydro-geomorphology study. Additionally, slope, aspect, drainage density, and lineament density were extracted from a Digital Elevation Model (DEM) using GIS and transformed into thematic layers. For training and validation, 114 springs were divided into a 70/30 ratio, with an equal number of non-spring pixels. After assigning weights to each class based on the two proposed models, a groundwater potential map was generated using GIS, classifying the area into five levels: very low, low, moderate, high, and very high. The model's outcome reveals that over 41% of the area falls into the low and very low potential categories, while only 30% of the area demonstrates a high probability of groundwater potential. To evaluate model performance, accuracy was assessed using the Area under the Curve (AUC). The success rate AUC values for the FR and SE methods were determined to be 78.73% and 77.09%, respectively. Additionally, the prediction rate AUC values for the FR and SE methods were calculated as 76.31% and 74.08%. The results indicate that the FR model exhibits greater prediction capability compared to the SE model in this case study.

Keywords: groundwater potential mapping, frequency ratio, Shannon’s Entropy, Lesser Himalaya Zone, sustainable groundwater management

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Authors: Z. Haddad, C. Abid, O. Rahli, O. Margeat, W. Dachraoui, A. Mataoui

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The present study aims to measure the volumetric mass density of NiPd-heptane nanofluids synthesized using a one-step method known as thermal decomposition of metal-surfactant complexes. The particle concentration is up to 7.55 g/l and the temperature range of the experiment is from 20°C to 50°C. The measured values were compared with the mixture theory and good agreement between the theoretical equation and measurement were obtained. Moreover, the available nanofluids volumetric mass density data in the literature is reviewed.

Keywords: NiPd nanoparticles, nanofluids, volumetric mass density, stability

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Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

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Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

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Authors: Alireza Zabihi, Saeed Peyghami, Hossein Mokhtari

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With the increasing deployment of renewable power plants, such as solar and wind, it is crucial to measure the harmonic situation of the grid. This paper proposes a global voltage harmonic index to measure the harmonic situation of the power grid with a focus on power transformers. The power electronics systems used to connect these plants to the network can introduce harmonics, leading to increased losses, reduced efficiency, false operation of protective relays, and equipment damage due to harmonic intensifications. The proposed index considers the losses caused by harmonics in power transformers which are of great importance and value to the network, providing a comprehensive measure of the harmonic situation of the grid. The effectiveness of the proposed index is evaluated on a real-world distribution network, and the results demonstrate its ability to identify the harmonic situation of the network, particularly in relation to power transformers. The proposed index provides a comprehensive measure of the harmonic situation of the grid, taking into account the losses caused by harmonics in power transformers. The proposed index has the potential to support power companies in optimizing their power systems and to guide researchers in developing effective mitigation strategies for harmonics in the power grid.

Keywords: global voltage harmonic index, harmonics, power grid, power quality, power transformers, renewable energy

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Arash Rahmani, Ahmad Ghanbari, Abbas Baghernezhad, Babak Safaei

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In the design of parallel manipulators, usually mean value of a dexterity measure over the workspace volume is considered as the objective function to be used in optimization algorithms. The mentioned indexes in a hybrid parallel manipulator (HPM) are quite complicated to solve thanks to infinite solutions for every point within the workspace of the redundant manipulators. In this paper, spatial isotropic design axioms are extended as a well-known method for optimum design of manipulators. An upper limit for the isotropy measure of HPM is calculated and instead of computing and minimizing isotropy measure, minimizing the obtained limit is considered. To this end, two different objective functions are suggested which are obtained from objective functions of comprising modules. Finally, by using genetic algorithm (GA), the best geometric parameters for a specific hybrid parallel robot which is composed of two modified Gough-Stewart platforms (MGSP) are achieved.

Keywords: hybrid manipulator, spatial isotropy, genetic algorithm, optimum design

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Authors: Zhihui Liu, Dongmei Hao, Qian Qiu, Yang An, Lin Yang, Song Zhang, Yimin Yang, Xuwen Li, Dingchang Zheng

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Premature birth, defined as birth before 37 completed weeks of gestation is a leading cause of neonatal morbidity and mortality and has long-term adverse consequences for health. It has recently been reported that the worldwide preterm birth rate is around 10%. The existing measurement techniques for diagnosing preterm delivery include tocodynamometer, ultrasound and fetal fibronectin. However, they are subjective, or suffer from high measurement variability and inaccurate diagnosis and prediction of preterm labor. Electrohysterography (EHG) method based on recording of uterine electrical activity by electrodes attached to maternal abdomen, is a promising method to assess uterine activity and diagnose preterm labor. The purpose of this study is to analyze the difference of EHG signal features between term labor and preterm labor. Free access database was used with 300 signals acquired in two groups of pregnant women who delivered at term (262 cases) and preterm (38 cases). Among them, EHG signals from 38 term labor and 38 preterm labor were preprocessed with band-pass Butterworth filters of 0.08–4Hz. Then, EHG signal features were extracted, which comprised classical time domain description including root mean square and zero-crossing number, spectral parameters including peak frequency, mean frequency and median frequency, wavelet packet coefficients, autoregression (AR) model coefficients, and nonlinear measures including maximal Lyapunov exponent, sample entropy and correlation dimension. Their statistical significance for recognition of two groups of recordings was provided. The results showed that mean frequency of preterm labor was significantly smaller than term labor (p < 0.05). 5 coefficients of AR model showed significant difference between term labor and preterm labor. The maximal Lyapunov exponent of early preterm (time of recording < the 26th week of gestation) was significantly smaller than early term. The sample entropy of late preterm (time of recording > the 26th week of gestation) was significantly smaller than late term. There was no significant difference for other features between the term labor and preterm labor groups. Any future work regarding classification should therefore focus on using multiple techniques, with the mean frequency, AR coefficients, maximal Lyapunov exponent and the sample entropy being among the prime candidates. Even if these methods are not yet useful for clinical practice, they do bring the most promising indicators for the preterm labor.

Keywords: electrohysterogram, feature, preterm labor, term labor

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Authors: Aïda C. Terblanché-Greeff

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Afro-polychronism is a unique amalgamated cultural value of social self-construal and time orientation. As such, the construct Afro-polychronism is conceptually analysed by focusing on the aspects of Ubuntu as collectivism and African time as polychronism. It is argued that these cultural values have a reciprocal and thus inseparable relationship. As it is general practice to measure cultural values empirically, the author conducted empirically engaged philosophy and aimed to develop a scale to measure Afro-polychronism based on its two dimensions of Ubuntu as social self-construal and African time as time orientation. From the scale’s psychometric properties, it was determined that the scale was, in fact, not reliable and valid. It was found that the correlation between the Ubuntu dimension and the African time is moderate (albeit statistically significant). In conclusion, the author abduced why this cultural value cannot be empirically measured based on its theoretical definition and indicated which different path would be more promising.

Keywords: African time, Afro-polychronism, empirically engaged African philosophy, Ubuntu

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Authors: Tooraj Karimi, Mohammadreza Sadeghi Moghadam

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Grey set theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for system study rather than traditional statistical regression which require massive data, normal distribution in the data and few variant factors. So, in this paper grey clustering and entropy of coefficient vector of grey evaluations are used to analyze energy consumption in buildings of the Oil Ministry in Tehran. In fact, this article intends to analyze the results of energy audit reports and defines most favorable characteristics of system, which is energy consumption of buildings, and most favorable factors affecting these characteristics in order to modify and improve them. According to the results of the model, ‘the real Building Load Coefficient’ has been selected as the most important system characteristic and ‘uncontrolled area of the building’ has been diagnosed as the most favorable factor which has the greatest effect on energy consumption of building. Grey clustering in this study has been used for two purposes: First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, grey clustering with variable weights has been used to classify all buildings in three categories named ‘no standard deviation’, ‘low standard deviation’ and ‘non- standard’. Entropy of coefficient vector of Grey evaluations is calculated to investigate greyness of results. It shows that among the 38 buildings surveyed in terms of energy consumption, 3 cases are in standard group, 24 cases are in ‘low standard deviation’ group and 11 buildings are completely non-standard. In addition, clustering greyness of 13 buildings is less than 0.5 and average uncertainly of clustering results is 66%.

Keywords: energy audit, grey set theory, grey incidence matrixes, grey clustering, Iran oil ministry

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Authors: F. Q. Shao, W. Q. Wang, Y. Yin, T. P. Yu, D. B. Zou, J. M. Ouyang

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The Radiation Pressure Acceleration (RPA) mechanism is very promising in laser-driven ion acceleration because of high laser-ion energy conversion efficiency. Although some experiments have shown the characteristics of RPA, the energy of ions is quite limited. The ion energy obtained in experiments is only several MeV/u, which is much lower than theoretical prediction. One possible limiting factor is the transverse instability incited in the RPA process. The transverse instability is basically considered as the Rayleigh-Taylor (RT) instability, which is a kind of interfacial instability and occurs when a light fluid pushes against a heavy fluid. Multi-dimensional particle-in-cell (PIC) simulations show that the onset of transverse instability will destroy the acceleration process and broaden the energy spectrum of fast ions during the RPA dominant ion acceleration processes. The evidence of the RT instability driven by radiation pressure has been observed in a laser-foil interaction experiment in a typical RPA regime, and the dominant scale of RT instability is close to the laser wavelength. The development of transverse instability in the radiation-pressure-acceleration dominant laser-foil interaction is numerically examined by two-dimensional particle-in-cell simulations. When a laser interacts with a foil with modulated surface, the internal instability is quickly incited and it develops. The linear growth and saturation of the transverse instability are observed, and the growth rate is numerically diagnosed. In order to optimize interaction parameters, a method of information entropy is put forward to describe the chaotic degree of the transverse instability. With moderate modulation, the transverse instability shows a low chaotic degree and a quasi-monoenergetic proton beam is produced.

Keywords: information entropy, radiation pressure acceleration, Rayleigh-Taylor instability, transverse instability

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Authors: Patrick Mbassegue, Mickael Gardoni

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In a market environment centered more and more on services and the digital economy, knowledge management becomes a framework that can help organizations to create value and to improve their overall performance. Based on an optimal allocation of scarce resources, managers are interested in demonstrating the added value generated by knowledge management projects. One of the challenges faced by organizations is the difficulty in measuring impacts and concrete results of knowledge management initiatives. The present article concerns the measure of concrete results coming from knowledge management projects based on balance scorecard model. One of the goals is to underline what can be done based on this model but also to highlight the limits associated. The present article is structured in five parts; 1-knowledge management projects and organizational impacts; 2- a framework and a methodology to measure organizational impacts; 3- application illustrated in two case studies; 4- limits concerning the proposed framework; 5- the proposal of a new framework to measure organizational impacts.

Keywords: knowledge management, project, balance scorecard, impacts

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Authors: Hamid Reza Zeraatpisheh, Ali Akbar Heidari, Soleiman Mohammadi Doust

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This study has examined the relationship between place attachment and sustainable development of urban spaces. To perform this, the components of place identity, emotional attachment, place attachment and social bonding which totally constitute the output of place attachment, by means of the standardized questionnaire measure place attachment in three domains of (cognitive) the place identity, (affective) emotional attachment and (behavioral) place attachment and social bonding. To measure sustainable development, three components of sustainable development, including society, economy and environment has been considered. The study is descriptive. The assessment instrument is the standard questionnaire of Safarnia which has been used to measure the variable of place attachment and to measure the variable of sustainable development, a questionnaire has been made by the researcher and been based on the combined theoretical framework. The statistical population of this research has been the city of Shiraz. The statistical sample has been Hafeziyeh. SPSS software has been used to analyze the data and examined the results of both descriptive and inferential statistics. In inferential statistics, Pearson correlation coefficient has been used to examine the hypotheses. In this study, the variable of place attachment is high and sustainable development is also in a high level. These results suggest a positive relationship between attachment to place and sustainable development.

Keywords: place attachment, sustainable development, economy-society-environment, Hafez's tomb

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Authors: Feddaoui Amina

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Income smoothing represents an attempt on the part of the company's management to reduce variations in earnings through the manipulation of the accounting principles. In this study, we aimed to measure income smoothing practices in a sample of 30 French joint stock companies during the period (2007-2009), we used Dummy variables method and “ECKEL” model to measure income smoothing practices and Binomial test accourding to SPSS program, to confirm or refute our hypothesis. This study concluded that there are no significant statistical indicators of income smoothing practices in the sample studied of French companies during the period (2007-2009), so the income series in the same sample studied of is characterized by stability and non-volatility without any intervention of management through accounting manipulation. However, this type of accounting manipulation should be taken into account and efforts should be made by control bodies to apply Eckel model and generalize its use at the global level.

Keywords: income, smoothing, 'Eckel', French companies

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Authors: Kang Won Lee, Jung Won Lee

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Characterizing the network flow is of fundamental importance to understand the complex dynamics of networks. And passenger flow characteristics of the subway network are very relevant for an effective transportation management in urban cities. In this study, passenger flow of Seoul metropolitan subway network is investigated and characterized through statistical analysis. Traditional betweenness centrality measure considers only topological structure of the network and ignores the transportation factors. This paper proposes a weighted betweenness centrality measure that incorporates monthly passenger flow volume. We apply the proposed measure on the Seoul metropolitan subway network involving 493 stations and 16 lines. Several interesting insights about the network are derived from the new measures. Using Kolmogorov-Smirnov test, we also find out that monthly passenger flow between any two stations follows a power-law distribution and other traffic characteristics such as congestion level and throughflow traffic follow exponential distribution.

Keywords: betweenness centrality, correlation coefficient, power-law distribution, Korea traffic DB

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Authors: J. B. Clapp, S. Croarkin, C. Dolphin, S. K. Lyons

Abstract:

Chronic pain or stress in farm animals impacts both on their welfare and productivity. Measuring chronic pain or stress can be problematic using hormonal or behavioural changes because hormones are modulated by homeostatic mechanisms and observed behaviour can be highly subjective. We propose that heart rate variability (HRV) can quantify chronic pain or stress in farmed animal and represents a more robust and objective measure of their welfare.

Keywords: dairy calf, welfare, heart rate variability, non-invasive, biomonitor

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Authors: Marcello De Falco, Gianluca Natrella, Mauro Capocelli

Abstract:

CO₂ capture and utilization (CCU) is a promising approach to reduce GHG(greenhouse gas) emissions. Many technologies in this field are recently attracting attention. However, since CO₂ is a very stable compound, its utilization as a reagent is energetic intensive. As a consequence, it is unclear whether CCU processes allow for a net reduction of environmental impacts from a life cycle perspective and whether these solutions are sustainable. Among the tools to apply for the quantification of the real environmental benefits of CCU technologies, exergy analysis is the most rigorous from a scientific point of view. The exergy of a system is the maximum obtainable work during a process that brings the system into equilibrium with its reference environment through a series of reversible processes in which the system can only interact with such an environment. In other words, exergy is an “opportunity for doing work” and, in real processes, it is destroyed by entropy generation. The exergy-based analysis is useful to evaluate the thermodynamic inefficiencies of processes, to understand and locate the main consumption of fuels or primary energy, to provide an instrument for comparison among different process configurations and to detect solutions to reduce the energy penalties of a process. In this work, the exergy analysis of a process for the production of Dimethyl Ether (DME) from green hydrogen generated through an electrolysis unit and pure CO₂ captured from flue gas is performed. The model simulates the behavior of all units composing the plant (electrolyzer, carbon capture section, DME synthesis reactor, purification step), with the scope to quantify the performance indices based on the II Law of Thermodynamics and to identify the entropy generation points. Then, a plant optimization strategy is proposed to maximize the exergy efficiency.

Keywords: green DME production, exergy analysis, energy penalties, exergy efficiency

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Authors: Jun Yang, Norbert Schulz, Claude Canizares

Abstract:

Knowing the dust content in the interstellar matter is necessary to understand the composition and evolution of the interstellar medium (ISM). The metal composition of the ISM enables us to study the cooling and heating processes that dominate the star formation rates in our Galaxy. The Chandra High Energy Transmission Grating (HETG) Spectrometer provides a unique opportunity to measure element dust compositions through X-ray edge absorption structure. We measure gas to dust optical depth ratios towards 9 bright Low-Mass X-ray Binaries (LMXBs) in the Galactic Bulge with the highest precision so far. Well calibrated and pile-up free optical depths are measured with the HETG spectrometer with respect to broadband hydrogen equivalent absorption in bright LMXBs: 4U 1636-53, Ser X-1, GX 3+1, 4U 1728-34, 4U 1705-44, GX 340+0, GX 13+1, GX 5-1, and GX 349+2. From the optical depths results, we deduce gas to dust ratios for various silicates in the ISM and present our results for the Si K edge in different lines of sight towards the Galactic Bulge.

Keywords: low-mass X-ray binaries, interstellar medium, gas to dust ratio, spectrometer

Procedia PDF Downloads 115

Authors: Bara M. Yousef

Abstract:

Kingdome of Saudi Arabia (KSA). High numbers of traffic accidents with sever, moderate and mild level of impairments admits to Sultan bin Abdulaziz humanitarian city. Over a period of 4 months the city received 111 male and 79 female subjects with CP, who received 4-6 weeks of rehabilitation and using WeeFIM score to measure rehabilitation outcomes. WeeFIM measures and covers various domains, such as: self-care, mobility, locomotion, communication and other psycho-social aspects. Our findings shed the light on the fact that nearly 85% of people at admission got better after rehabilitation program services at individual sever moderate and mild and has arrange of (59 out of 128 WeeFIM score) and by the time of discharge they leave the city with better FIM score close to (72 out of 128 WeeFIM score) for the entire study sample. WeeFIM score is providing fair evidence to rehabilitation specialists to assess their outcomes. However there is a need to implement other instruments and compare it to WeeFIM in order to reach better outcomes at discharge level.

Keywords: Cerepral Palsy (CP), pediatric Functional Independent Measure (WeeFIM), rehabilitation, disability

Procedia PDF Downloads 195

Authors: Burçak Boztemur, Yue Xu, Laima Luo, M. Lütfi Öveçoğlu, Duygu Ağaoğulları

Abstract:

Tungsten (W) is used chiefly as plasma-facing material. However, it has some problems, such as brittleness after plasma exposure. High-entropy alloys (RHEAs) are a new opportunity for this deficiency. So, the neutron shielding behavior of WNbMoVTa and WNbMoVTaCr₀.₅Al compositions were examined against He⁺ irradiation in this study. The mechanical and irradiation properties of the WNbMoVTa base composition were investigated by adding the Al and Cr elements. The mechanical alloying (MA) for 6 hours was applied to obtain RHEA powders. According to the X-ray diffraction (XRD) method, the body-centered cubic (BCC) phase and NbTa phase with a small amount of WC impurity that comes from vials and balls were determined after 6 h MA. Also, RHEA powders were consolidated with the spark plasma sintering (SPS) method (1500 ºC, 30 MPa, and 10 min). After the SPS method, (Nb,Ta)C and W₂C₀.₈₅ phases were obtained with the decomposition of WC and stearic acid that is added during MA based on XRD results. Also, the BCC phase was obtained for both samples. While the Al₂O₃ phase with a small intensity was seen for the WNbMoVTaCr₀.₅Al sample, the Ta₂VO₆ phase was determined for the base sample. These phases were observed as three different regions according to scanning electron microscopy (SEM). All elements were distributed homogeneously on the white region by measuring an electron probe micro-analyzer (EPMA) coupled with a wavelength dispersive spectroscope (WDS). Also, the grey region of the WNbMoVTa sample was rich in Ta, V, and O elements. However, the amount of Al and O elements was higher for the grey region of the WNbMoVTaCr₀.₅Al sample. The high amount of Nb, Ta, and C elements were determined for both samples. Archimedes’ densities that were measured with alcohol media were closer to the theoretical densities of RHEAs. These values were important for the microhardness and irradiation resistance of compositions. While the Vickers microhardness value of the WNbMoVTa sample was measured as ~11 GPa, this value increased to nearly 13 GPa with the WNbMoVTaCr₀.₅Al sample. These values were compatible with the wear behavior. The wear volume loss was decreased to 0.16×10⁻⁴ from 1.25×10⁻⁴ mm³ by the addition of Al and Cr elements to the WNbMoVTa. The He⁺ irradiation was conducted on the samples to observe surface damage. After irradiation, the XRD patterns were shifted to the left because of defects and dislocations. He⁺ ions were infused under the surface, so they created the lattice expansion. The peak shifting of the WNbMoVTaCr₀.₅Al sample was less than the WNbMoVTa base sample, thanks to less impact. A small amount of fuzz was observed for the base sample. This structure was removed and transformed into a wavy structure with the addition of Cr and Al elements. Also, the deformation hardening was actualized after irradiation. A lower amount of hardening was obtained with the WNbMoVTaCr₀.₅Al sample based on the changing microhardness values. The surface deformation was decreased in the WNbMoVTaCr₀.₅Al sample.

Keywords: refractory high entropy alloy, microhardness, wear resistance, He⁺ irradiation

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